A Resource-Efficient Placement of Edge Servers for Green Agriculture Consumer Electronics

Against the backdrop of global carbon neutrality and low-carbon agriculture, the urgency to promote low-carbon agricultural consumer electronics through the integration of sustainable computing is increasingly evident. Edge servers, with their high efficiency and low latency characteristics, have become a crucial component of sustainable computing. Using their local deployment and low-latency advantages, edge servers enable a real-time decision optimization system, optimize energy-efficient resource scheduling, reduce carbon emissions in the agricultural production process, and thereby facilitate low-carbon agriculture. However, for edge computing to deliver efficient, low-latency, and low-energy services, it must rely on the strategic allocation of edge servers. Suboptimal deployment strategies can result in elevated network delays, diminished service reliability, and higher levels of carbon output. The problem of identifying the most effective locations for deploying a limited number of edge servers, while addressing key performance concerns such as latency, reliability, and environmental impact under practical constraints, is commonly known as the kESP problem. Recent research has addressed issues such as high latency, low robustness, and carbon emission reduction in edge computing networks, but has yet to simultaneously reduce latency, improve robustness, and optimize computing resources while lowering carbon emissions. To tackle this challenge, we introduce the kESP-PSO approach, designed to mitigate high latency, enhance service reliability, and reduce carbon emissions by determining an efficient deployment strategy for edge servers. Specifically, kESP-PSO method incorporates a Particle Swarm Optimization (PSO) algorithm, which iteratively refines the location of edge servers based on the spatial distribution of base stations and mobile users across the target region. Through this mechanism, kESP-PSO is capable of theoretically deriving the most effective configuration of edge server placements. Extensive experiments on Melbourne and Shanghai Telecom data sets demonstrate that the proposed method significantly reduces carbon emissions compared to baseline approaches, while also optimizing computing resources and effectively supporting low-carbon agricultural consumer electronics.